Application of Flow and Transport Optimization Codes to Groundwater Pump and Treat Systems- Volume III

Minsker, Barbara; Zhang, Yan; Greenwald, Robert; Peralta, Richard C.; Chen, Kewei; Harre, Karla; Becker, D.L.; Yeh, Laura; Yager, Kathy

doi:10.21236/ada423251

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…Peralta et al (2002) detail the followed procedures and results. Minsker et al (2004) summarize the ESTCP project goals, study sites, and results.…”

Section: Description Of the Case Study And Formulationmentioning

confidence: 99%

Simulation/Optimization Modeling for Robust Pumping Strategy Design

Kalwij

Peralta²

2006

Groundwater

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A new simulation/optimization modeling approach is presented for addressing uncertain knowledge of aquifer parameters. The Robustness Enhancing Optimizer (REO) couples genetic algorithm and tabu search as optimizers and incorporates aquifer parameter sensitivity analysis to guide multiple-realization optimization. The REO maximizes strategy robustness for a pumping strategy that is optimal for a primary objective function (OF), such as cost. The more robust a strategy, the more likely it is to achieve management goals in the field, even if the physical system differs from the model. The REO is applied to trinitrotoluene and Royal Demolition Explosive plumes at Umatilla Chemical Depot in Oregon to develop robust least cost strategies. The REO efficiently develops robust pumping strategies while maintaining the optimal value of the primary OF-differing from the common situation in which a primary OF value degrades as strategy reliability increases. The REO is especially valuable where data to develop realistic probability density functions (PDFs) or statistically derived realizations are unavailable. Because they require much less field data, REO-developed strategies might not achieve as high a mathematical reliability as strategies developed using many realizations based upon real aquifer parameter PDFs. REO-developed strategies might or might not yield a better OF value in the field.

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“…Peralta et al (2002) detail the followed procedures and results. Minsker et al (2004) summarize the ESTCP project goals, study sites, and results.…”

Section: Description Of the Case Study And Formulationmentioning

confidence: 99%

Simulation/Optimization Modeling for Robust Pumping Strategy Design

Kalwij

Peralta²

2006

Groundwater

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Multiscale island injection genetic algorithms for groundwater remediation

Sinha

Minsker

2007

Advances in Water Resources

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Uncertainty‐based multiobjective optimization of groundwater remediation design

Singh

Minsker

2008

Water Resources Research

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[1] Management of groundwater contamination often involves conflicting objectives and substantial uncertainty. The primary objective of this paper is to present a new approach, called the probabilistic multiobjective genetic algorithm (PMOGA), which incorporates uncertainty with multiobjective Pareto optimization. This type of approach is needed for water resources problems with multiple uncertain objectives; previous uncertainty-based optimization approaches consider uncertainty only in the constraints (typically constraining reliability with respect to environmental standards) or in a single objective. The optimization algorithm uses a probabilistic ranking and crowding scheme that improves decision making within the genetic algorithm by identifying a set of solutions that are Pareto optimal despite the uncertainty in the objective values. The algorithm is applied to two groundwater remediation test cases with uncertain objectives: a hypothetical case study and a field-scale pump-and-treat design problem at the Umatilla Chemical Depot situated at Hermiston, Oregon. For these case studies the primary source of uncertainty stems from uncertain aquifer hydraulic conductivity values, which affect both the remediation cost and efficiency. Results are analyzed, and the advantages of the PMOGA are discussed relative to an averaging-based multiobjective approach, a stochastic single-objective approach (similar to chance constrained optimization), and a deterministic multiobjective approach. The results demonstrate that using such an uncertainty-based multiobjective optimization scheme can give valuable information about remediation options, giving results that are not only cost effective but that also have lower uncertainty.Citation: Singh, A., and B. S. Minsker (2008), Uncertainty-based multiobjective optimization of groundwater remediation design,

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Application of Flow and Transport Optimization Codes to Groundwater Pump and Treat Systems- Volume III

Cited by 3 publications

References 2 publications

Simulation/Optimization Modeling for Robust Pumping Strategy Design

Simulation/Optimization Modeling for Robust Pumping Strategy Design

Multiscale island injection genetic algorithms for groundwater remediation

Uncertainty‐based multiobjective optimization of groundwater remediation design

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